This method has the potential to be very fast.

because the performance of this method is restricted to the K-best list, we may have to set K to a high number in order to find the best parsing tree (with DLM) or a tree acceptably close to the best (Shen et al., 2008).


On one hand, the heterogeneous solver provides structural information, which is the basis to construct the sub-word sequence.

this tagger provides additional POS information, which is helpful for disambiguation.


The stacking models can be viewed as data-driven annotation converting models.

they are not trained on "real" labeled samples.


From this table, we can see that words with low frequency, especially the out-of-vocabulary (OOV) words, are hard to label.

when a word is very frequently used, its behavior is very complicated and therefore hard to predict.


We choose to work with these two algorithms considering their prior success in other NLP applications.

we expect that our approach can function with other clustering algorithms.


A natural strategy for extending current experiments is to include both clustering results together, or to include more than one cluster granularity.


However, a disadvantage of the perceptron style systems is that they can not provide probabilistic information.

new word detection is also one of the important problems in Chinese information processing.


There were studies trying to solve this problem jointly with CWS.


As we can see, the new features improved performance on both word segmentation and new word detection.

we also noticed that the training cost became more expensive via adding high dimensional new features.


On one hand, this is  Table 3: VerbNet accuracy without the none class generally in contrast with standard text categorization tasks, for which n-gram models show accuracy comparable to the simpler BOW.

it simply confirms that verb classification requires the dependency information between words (i.e., at least the sequential structure information provided by SK).


Second, SK is 2.56 percent points below the stateof-the-art achieved in (Brown et al., 2011) (BR), i.e, 82.08 vs. 84.64.

sTK applied to our representation (CT, GRCT and LCT) produces comparable accuracy, e.g., 84.83, confirming that syntactic representation is needed to reach the state-of-the-art.


Overall, IR systems can potentially benefit from the correct meanings of words provided by WSD systems.

in previous investigations of the usage of WSD in IR, different researchers arrived at conflicting observations and conclusions.


Similarly, Harmeling (2009) suggested a heuristic set of 28 transformations, which include various types of node-substitutions as well as restructuring of the entire parse-tree.

to such predefined sets of transformations, knowledge oriented approaches were sug-gested by Bar-Haim et al.


In addition, they used knowledge-based lexical substitutions.

when only knowledge-based transformations are allowed, transforming the text into the hypothesis is impossible in many cases.


The results, presented in Table 6, show that by avoiding any type of lookahead one can achieve fast runtime, while compromising proof quality.

both exhaustive and local lookahead yield better proofs and accuracy, while local lookahead is more than 4 times faster than exhaustive lookahead.


(2009) improved a syntactic SMT system by adding as many as ten thousand syntactic features, and used Margin Infused Relaxed Algorithm (MIRA) to train the feature weights.

the number of parameters in common phrase and lexicon translation models is much larger.


To prevent overfitting, the statistics of phrase pairs from a particular sentence was excluded from the phrase table when aligning that sentence.

as pointed out by Liang et al (2006), the same problem as in the bold updating existed, i.e., forced alignment between a source sentence and its reference translation was tricky, and the proposed alignment was likely to be unreliable.


Projection step In this step, we compute: This moves θ in the direction of steepest descent (∇F) with step size s, and then the function [•] ∆ projects the resulting point onto the simplex; that is, it finds the nearest point that satisfies the constraints (8).

the gradient ∇F(θ k ) is to Schoenemann (2011b), we use an O(n log n) algorithm for the projection step due to Duchi et.


The fact that we had to use hand-aligned data to tune the hyperparameters α and β means that our method is no longer completely unsupervised.

our observation is that alignment accuracy is actually fairly robust to the choice of these hyperparameters, as shown in Table 2.


We find that TME can discover and cluster many correct Cexpressions, e.g., "great review", "review helped me" in Thumbs-up; "poor review", "very unfair review" in Thumbs-down; "how do I", "help me decide" in Question; "good reply", "thank you for clarifying" in Answer Acknowledgement; "I disagree", "I refute" in Disagreement; and "I agree", "true in fact" in Agreement.

with the guidance of Max-Ent priors, ME-TME did much better (Table 2).


Language understanding has been well studied in the context of question/answering (Harabagiu and Hickl, 2006;Liang et al., 2011), entailment (Sammons et al., 2010), summarization (Hovy et al., 2005;Daumé-III and Marcu, 2006), spoken language understanding (Tur and Mori, 2011;Dinarelli et al., 2009), query understanding (Popescu et al., 2010;Li, 2010;Reisinger and Pasca, 2011), etc.

data sources in VPA systems pose new challenges, such as variability and ambiguities in natural language, or short utterances that rarely contain contextual information, etc.


Their discriminative approach represents semantic slots and discourse-level utterance labels (domain or dialog act) in a single structure to encode dependencies.

their model requires fully labeled utterances for training, which can be time consuming and expensive to generate for dynamic systems.


Topic models are often evaluated quantitatively using perplexity and likelihood on held-out test data (Blei et al., 2003).

perplexity does not reflect our purpose since our aim is not to predict whether an unseen document is likely to be a review of some particular aspect.


Most information extraction (IE) systems identify facts that are explicitly stated in text.

in natural language, some facts are implicit, and identifying them requires "reading between the lines".


", standard IE systems cannot identify the answer since citizenship is not explicitly stated in the text.

a human reader possesses the commonsense knowledge that the president of a country is almost always a citizen of that country, and easily infers the correct answer.


In our task, the supervised training data consists of facts that are extracted from the natural language text.

we usually do not have evidence for inferred facts as well as noisy-or nodes.


Despite the fairly large size of the overall training sets (9,000 documents), the amount of data for each target relation is apparently still not sufficient to learn particularly accurate weights for both BLPs and MLNs.

for BLPs, learned weights do show a substantial improvement initially (i.e.


Between BLPs and MLNs, BLPs perform substantially better than MLNs at most points in the curve.

mLN-manual-Weights improve marginally over BLP-Learned-Weights at later points (top 600 and above) on the curve, where the precision is generally very low.


For BLPs, as n increases towards including all of the logically sanctioned inferences, as expected, the precision converges to the results for logical deduction.

as n decreases, both adjusted and unadjusted precision increase fairly steadily.


In BLPs, only propositions that can be logically deduced from the extracted evidence are included in the ground network.

mLNs include all possible type-consistent groundings of all rules in the network, introducing many ground literals which cannot be logically deduced from the evidence.


Lack of sufficient training data is one of the reasons for learning less accurate weights by the MLN weight learner.

a more important issue is due to the use of the closed world assumption during learning, which we believe is adversely impacting the weights learned.


32.8% HMM), if images are not presented.

the difference is less pronounced when images are shown.


For example, the Alignment feature in Figure 2(a) is local, and thus can be computed a priori, but the Word Trigrams is not; in Figure 2(b) words in parentheses are subgenerations created so far at each word node; their combination gives rise to the trigrams serving as input to the feature.

this combination may not take place at their immediate ancestors, since these may not be adjacent nodes in the hypergraph.


In this section, we describe a model that captures arbitrarily deep hierarchies over such layers of coreference decisions, enabling efficient inference and rich entity representations.

to the pairwise model, where each entity is a flat cluster of mentions, our proposed model structures each entity recursively as a tree.


Subentities provide a tighter granularity of coreference and can be used to perform larger block moves during MCMC.


(2010) uses streaming clustering for large-scale coreference.

the greedy nature of the approach does not allow errors to be revisited.


There is also work on end-to-end coreference resolution that uses large noun-similarity lists (Daumé III and Marcu, 2005) or structured knowledge bases such as Wikipedia (Yang and Su, 2007;Haghighi and Klein, 2009;Kobdani et al., 2011) and YAGO (Rahman and Ng, 2011).

such structured knowledge bases are of limited scope, and, while Haghighi and Klein (2010) self-acquires knowledge about coreference, it does so only via reference constructions and on a limited scale.


Many advantages have been claimed for decision tree classifiers, including interpretability and robustness.

we suspect that the aspect most relevant to our case is that decision trees can capture non-linear interactions between features.


The Entropy of a cluster reflects how the members of the k distinct subgroups are distributed within each resulting cluster; the global quality measure is computed by averaging the entropy of all clusters: (2) where P (i, j) is the probability of finding an element from the category i in the cluster j, n j is the number of items in cluster j, and n the total number of items in the distribution.

to purity, the entropy decreases as the quality of clustering improves.


Previous works have showed that supervised learning methods are superior for this task.

the performance of supervised methods highly relies on manually labeled training data.


There are also lots of studies for cross-domain sentiment analysis (Blitzer et al., 2007;Tan et al., 2007;Li et al., 2009;Bollegala et al., 2011;He et al., 2011;Glorot et al., 2011).

most of them focused on coarse-grained document-level sentiment classification, which is different from our fine-grained word-level extraction.


We present a novel approach for building verb subcategorization lexicons using a simple graphical model.

to previous methods, we show how the model can be trained without parsed input or a predefined subcategorization frame inventory.


Large, manually-constructed SCF lexicons mostly target general language (Boguraev and Briscoe, 1987;Grishman et al., 1994).

in many domains verbs exhibit different syntactic behavior (Roland and Jurafsky, 1998;Lippincott et al., 2010).


Traditional learning approaches have relied on access to parallel corpora of natural language sentences paired with their meanings (Mooney, 2007;Zettlemoyer and Collins, 2007;Lu et al., 2008;Kwiatkowski et al., 2010).

constructing such semantic annotations can be difficult and time-consuming.


In this paper we presented a novel online algorithm for building a lexicon from ambiguously supervised relational data.

to the previous approach that computed common subgraphs between different contexts in which an n-gram appeared, we instead focus on small, connected subgraphs and introduce an algorithm, SGOLL, that is an order of magnitude faster.


Furthermore, when a sentence is unparsable with large tree fragments, the PTSG parser usually uses naive CFG rules derived from its backoff model, which diminishes the benefits obtained from large tree fragments.

current state-of-the-art parsers use symbol refinement techniques (Johnson, 1998;Collins, 2003;Matsuzaki et al., 2005).


An adaptor grammar (Johnson et al., 2007a) is a sort of nonparametric Bayesian TSG model with symbol refinement, and is thus closely related to our SR-TSG model.

an adaptor grammar differs from ours in that all its rules are complete: all leaf nodes must be terminal symbols, while our model permits nonterminal symbols as leaf nodes.


In recent years, statistical machine translation(SMT) has been rapidly developing with more and more novel translation models being proposed and put into practice (Koehn et al., 2003;Och and Ney, 2004;Galley et al., 2006;Liu et al., 2006;Chiang, 2007;Chiang, 2010).

similar to other natural language processing(NLP) tasks, SMT systems often suffer from domain adaptation problem during practical applications.


Using these models, the words can be clustered into the derived topics with a probability distribution, and the correlation between words can be automatically captured via topics.

the "bag-of-words" assumption is an unrealistic oversimplification because it ignores the order of words.


Here, t f out is the topic clustered from the corpus C f out , and t f in represents the topic derived from the corpus C f in .

the two above-mentioned probabilities can not be directly multiplied in formula (4) because they are related to different topic spaces from different corpora.


A simple way to compute the phrase-topic distribution is to take the fractional counts from C f in and then adopt MLE to obtain relative probability.

it is infeasible in our model because some phrases occur in C f out while being absent in C f in .


For this experimental result, we speculate that with the increment of in-domain monolingual data, the corresponding topic models provide more accurate topic information to improve the translation system.

this effect weakens when the monolingual corpora continue to increase.


Our transliteration mining model can mine transliterations without using any labelled data.

if there is some labelled data available, our system is able to use it effectively.


Our unsupervised transliteration mining system can be applied to language pairs for which no labelled data is available.

the unsupervised system is focused on high recall and also mines close transliterations (see Section 5 for details).


Thanks to sophisticated reordering models, stateof-the-art PSMT systems are generally good at handling local reordering phenomena that are not captured by phrase-internal reordering.

they typically fail to predict long reorderings.


The lexicalization is effective and increases the maximal rank (number of arguments) of the nonterminals by at most 1.

to a transformation into GREIBACH normal form, our lexicalization does not radically change the structure of the derivations.


The nonterminals of regular tree grammars only occur at the leaves and are replaced using first-order substitution.

the nonterminals of a CFTG are ranked symbols, can occur anywhere in a tree, and are replaced using second-order substitution.


The co-ranking framework has been initially developed for measuring scientific impact and modeling the relationship between authors and their publications (Zhou et al., 2007).

the adaptation of this framework to the tweet recommendation task is novel to our knowledge.


, r w is then ∏ w i=1 r i (t) under a given t. The value of t is chosen when the probability is maximized: In a simple random walk, it is assumed that all nodes in the matrix M are equi-probable before the walk.

we use the topic preference vector as a prior on M. Let Diag(r) denote a diagonal matrix whose eigenvalue is vector r. Then m becomes: Diversity We would also like our output to be diverse without redundant information.


In theory, we could also apply DivRank on the author graph.

as the authors are unique, we assume that they are sufficiently distinct and there is no need to promote diversity.


(2011) combine a classifier based on the k-nearest neighbors algorithm with a CRFbased model to leverage cross tweets information, and adopt the semi-supervised learning to leverage unlabeled tweets.

named entity normalization (NEN) for tweets, which transforms named entities mentioned in tweets to their unambiguous canonical forms, has not been well studied.


We introduce the task of NEN for tweets, a new genre of texts with rich entity variations.

to existing NEN systems, which take the output of NER systems as their input, our method conducts NER and NEN at the same time, allowing them to reinforce each other, as demonstrated by the experimental results.


As an illustrative example, consider the following three tweets: " Gaga" are all labeled as PERSON, and can be restored as "Lady Gaga".

to existing work, our method jointly conducts NER and NEN for multiple tweets.


Note that not every topic has a bursty interval.

a topic may have multiple bursty intervals and hence leads to multiple bursty topics.


* indicates an edge feature The linear-chain CRFs can represent the dependency between adjacent target sentences quite well.

they cannot model the dependency between adjacent source sentences, because labeling is done for each source sentence individually.


(2010) mined query logs to find attributes of entity instances.

these projects did not learn relative probabilities of different senses.


Because they only had four types, they were able to hand label their training data.

our system self-labels training examples by searching query logs for high-likelihood entities, and must handle any errors introduced by this process.


We compute this quantity exactly by evaluating the joint for each combination of t and i, and the observed values of q and c. It is important to note that at runtime when a new query is issued, we have to resolve the entity in the absence of any observed click.

we do have access to historical click probabilities, P (c | q).


For English sentiment classification, there are several labeled corpora available (Hu and Liu, 2004;Pang et al., 2002;Wiebe et al., 2005).

labeled resources in other languages are often insufficient or even unavailable.


Para-Cotrain: The training process is the same as MT-Cotrain.

we use a different set of English unlabeled sentences.


When 3500 labeled sentences are used, SVM achieves 80.58%, a relatively high accuracy for sentiment classification.

cLMM and the other two models can still gain improvements.


The asker wishes to know why his teeth bloods and how to prevent it.

the best answer only gives information on the reason of teeth blooding.


These error mining techniques have been applied with good results on parsing output and shown to help improve the large scale symbolic grammars and lexicons used by the parser.

the techniques they use (e.g., suffix arrays) to enumerate and count n-grams builds on the sequential nature of a text corpus and cannot easily extend to structured data.


In this task, the challenge is to find a replacement for a word or phrase removed from a sentence.

to our SAT-inspired task, the original answer is indicated.


A larger m indicates a more accurate heuristic, which results in a more efficient A* search (fewer nodes being processed).

this efficiency comes with the price that such an accurate heuristic requires more computation time in the Viterbi backward pass.


We refer to this setting as bootstrapping.

typical semi-supervised learning deals with a large number of labelled points, and a domain adaptation task with unlabelled points from the new domain.


Being largely languageuniversal, the selection component is learned in a supervised fashion from all the training languages.

the ordering decisions are only influenced by languages with similar properties.


In this paper, we present a new multilingual algorithm for dependency parsing.

to previous approaches, this algorithm can learn dependency structures using annotations from a diverse set of source languages, even if this set is not related to the target language.


Overall, our method performs similarly to this oracle variant.

the gain for non Indo-European languages is 1.9% vs -1.3% for Indo-European languages.


Overall, the performance gap between the selective sharing model and its monolingual supervised counterpart is 7.3%.

the unsupervised monolingual variant of our model achieves a meager 26%.


Metalanguage is an essential linguistic mechanism which allows us to communicate explicit information about language itself.

it has been underexamined in research in language technologies, to the detriment of the performance of systems that could exploit it.


The adoption of this definition was motivated by a desire to study mentioned language with precise, repeatable results.

it was too abstract to consistently apply to large quantities of candidate phrases in sentences, a necessity for corpus creation.


A human reader with some knowledge of the usemention distinction can often intuit the presence of mentioned language in a sentence.

to operationalize the concept and move toward corpus construction, it was necessary to create a rubric for labeling it.


These results were taken as moderate indication of the reliability of "simple" use-mention labeling.

the per-category results showed reduced levels of agreement.


8  They use Kabuki precisely because they and everyone else have only a hazy idea of the word's true meaning, and they can use it purely on the level of insinuation.

9 the connection between mentioned language and stylistic cues is only valuable when stylistic cues are available.


(2004), who created a corpus of metalanguage from a subset of the British National Corpus, finding that approximately 11% of spoken utterances contained some form (whether explicit or implicit) of metalanguage.

limitations in the Anderson corpus' structure (particularly lack of word-or phrase-level annotations) and content (the authors admit it is noisy) served as compelling reasons to start afresh and create a richer resource.


Most of the current statistical approaches to SRL are supervised, requiring large quantities of human annotated data to estimate model parameters.

such resources are expensive to create and only available for a small number of languages and domains.


Inducing them solely based on monolingual data, though possible, may be tricky as selectional preferences of the roles are not particularly restrictive; similar restrictions for patient and agent roles may further complicate the process.

both sentences (a) and (b) are likely to be translated in German as '[ A0 Peter] beschuldigte [ A1 Mary] [ A2 einen Diebstahl zu planen]'.


Transition-based parsing algorithms, such as shiftreduce algorithms (Nivre, 2004;Zhang and Clark, 2008), are widely used for dependency analysis because of the efficiency and comparatively good performance.

these parsers have one major problem that they can handle only local information.


Finally, the combined treebank are used to train a better parser.

the inconsistencies among different treebanks are normally nontrivial, which makes rule-based conversion infeasible.


handling syntactic annotation inconsistencies is significantly more challenging in our case of parsing.


Their experiments show that the combined treebank can significantly improve the performance of constituency parsers.

their method requires several sophisticated strategies, such as corpus weighting and score interpolation, to reduce the influence of conversion errors.


Our approach is also intuitively related to stacked learning (SL), a machine learning framework that has recently been applied to dependency parsing to integrate two main-stream parsing models, i.e., graph-based and transition-based models (Nivre and McDonald, 2008;Martins et al., 2008).

the SL framework trains two parsers on the same treebank and therefore does not need to consider the problem of annotation inconsistencies.


For the sports dataset, since mentions contain person and organization named entity types, our score for clustering uses the Jaccard distance between context words of the mentions.

such clusterings do not produce columns.


All of these models jointly label aligned source and target sentences.

our model is not concerned with tagging English sentences but only tags foreign sentences in the context of English sentences.


fine-grained entity types of two arguments, to handle polysemy.

such fine grained entity types come at a high cost.


To differentiate between these senses we need types such as "Politician" or "Athlete".

for "A, the parent of B" we only need to distinguish between persons and organizations (for the case of the sub-organization relation).


They cast the relaxed assumption as multi-instance learning.


In these expressions, all information needed for normalization is contained in the linguistic expression.

absolute dates are relatively infrequent in our corpus (7%), so in order to broaden the coverage for the detection of salient dates, we decided to consider relative dates, which are far more frequent.


In case of TEs with a granularity superior to the day or month, DD and MM fields remain unspecified accordingly.

these underspecified dates are not used in our experiments.


Many scientific subjects, such as psychology, learning sciences, and biology, have adopted computational approaches to discover latent patterns in large scale datasets (Chen and Lombardi, 2010;Baker and Yacef, 2009).

the primary methods for historical research still rely on individual judgement and reading primary and secondary sources, which are time consuming and expensive.


Such models first estimate word translation probabilities conditioned on topics, and then adapt lexical weights of phrases * Corresponding author by these probabilities.

the state-of-theart SMT systems translate sentences by using sequences of synchronous rules or phrases, instead of translating word by word.


We call such a rule a topicinsensitive rule.

the distributions of the rest rules peak on a few topics.


As described in the previous section, we also estimate the target-side rule-topic distribution.

only source document-topic distributions are available during decoding.


In addition to the traditional hiero system, we also compare with the topic-specific lexicon translation method in Zhao and Xing (2007).

the lexicon translation probability is adapted by: we simplify the estimation of p(e|f, z = k) by directly using the word alignment corpus with  table 3: Percentage of topic-sensitive rules of various types of rule according to source-side ("Src") and targetside ("tgt") topic distributions.


The computational complexity of PTK is O(pρ (Moschitti, 2006a), where p is the largest subsequence of children that we want consider and ρ is the maximal outdegree observed in the two trees.

the average running time again tends to be linear for natural language syntactic trees (Moschitti, 2006a).


BOL cannot capture the same dependencies as the structural kernels.

when we remove the dependencies generated by shared documents between a node and its descendants (child-free setting) BOL improves on BL.


Prepositions and conjunctions are often assumed to depend on lexical dependencies for correct resolution (Jurafsky and Martin, 2008).

lexical statistics based on the training set only are typically sparse and have only a small effect on overall parsing performance (Gildea, 2001).


Unlabeled data has been shown to improve the accuracy of conjunctions within complex noun phrases Bergsma et al., 2011).

it has so far been less effective within full parsing -while first-order web-scale counts noticeably improved overall parsing in Bansal and Klein (2011), the accuracy on conjunctions actually decreased when the web-scale features were added (Table 4 in that paper).


some attachments like configuration ADJ and NdeN, for which the parser showed very good accuracy (96.6 and 92.2) show very poor performances.


Developing a supervised or semi-supervised model of discourse segmentation would require ground truth annotated based on a well-established representation scheme, but as of right now no such annotation exists for Chinese to the best of our knowledge.

syntactically annotated treebanks often contain important clues that can be used to infer discourse-level information.


Both Nissim (2006) and Rahman and Ng (2011) classify each mention individually in a standard supervised ML setting, not considering potential dependencies between the IS categories of different mentions.

collective or joint classification has made substantial impact in other NLP tasks, such as opinion mining (Pang and Lee, 2004;Somasundaran et al., 2009), text categorization (Yang et al., 2002;Taskar et al., 2002) and the related task of coreference resolution (Denis and Baldridge, 2007).


At first, this seems to contradict studies such as Cahill and Riester (2009) that find a variety of precedences according to information status.

many of the clearest precedences they find are more specific variants of the old > p mediated or old > p new precedence or they are preferences at an even finer level than the one we annotate, including for example the identification of generics.


Rule-based systems (Castano and de Antonellis, 1999;Milo and Zohar, 1998;L. Palopol and Ursino, 1998) often utilize only the schema information (e.g., elements, domain types of schema elements, and schema structure) to define a similarity metric for performing matching among the schema elements in a hard coded fashion.

learning based approaches learn a similarity metric based on both the schema information and the data.


High-quality senseannotated data, however, are hard to be obtained in streaming environments, since the training corpus would have to be constantly updated in order to accomodate the fresh data coming on the stream.

few positive examples plus large amounts of unlabeled data may be easily acquired.


For example, on a TAC corpus with 1.8M documents, we found that increasing the corpus size ten-fold consistently results in statistically significant improvement in F1 on two standardized relation extraction metrics (t-test with p=0.05).

increasing human feedback amount ten-fold results in statistically significant improvement on F1 only when the corpus contains at least 1M documents; and the magnitude of such improvement was only one fifth compared to the impact of corpus-size increment.


The locations of +'s suggest that the influence of human feedback becomes notable only when the corpus is very large (say with 10 6 docs).

comparing the slopes of the curves in Figure 3  tably improve precision on either the full corpus or on a small 1K-doc corpus.


:  Figure 1 can be obtained from various sources such as dictionaries, gazetteers, rule-based systems (Strötgen and Gertz, 2010), statistically trained classifiers (Ratinov and Roth, 2009), or some web resources such as Wikipedia (Ratinov et al., 2011).

in practice, outputs from existing mention identification and typing systems can be far from ideal.


One important clue is that March appears after the word in and is located nearer to other mentions that can be potentially useful arguments.

encoding such information as a general constraint can be inappropriate, as potentially better structures can be found if one considers other alternatives.


Out of those 6 patterns, 2 are more general patterns shared across different events, and 4 are event-specific.

for example, for the "Die" event, the supervised approach requires human to select from 174 candidate mentions and annotate 89 of them.


This indicates that our model is able to learn to generalize with features through the guidance of our informative preferences.

we also note that the performance of preference modeling depends on the actual quality and amount of preferences used for learning.


The model has been shown to work in unsupervised tasks such as POS induction (Smith and Eisner, 2005a), grammar induction (Smith and Eisner, 2005b), and morphological segmentation (Poon et al., 2009), where good neighborhoods can be identified.

it is less intuitive what constitutes a good neighborhood in this task.


Therefore, PLSA finds a topic distribution for each concept definition that maximizes the log likelihood of the corpus X (LDA has a similar form): In this formulation, missing words do not contribute to the estimation of sentence semantics, i.e., excluding missing words (X ij = 0) in equation 1 does not make a difference.

empirical results show that given a small number of observed words, usually topic models can only find one topic (most evident topic) for a sentence, e.g., the concept definitions of bank#n#1 and stock#n#1 are assigned the financial topic only without any further discernability.


While this is the ideal data set for SS, the small size makes it impossible for tuning SS algorithms or deriving significant performance conclusions.

the MSR04 data set comprises a much larger set of sentence pairs: 4,076 training and 1,725 test pairs.


Generally results are reported based on the last iteration.

we observe that for model 6 in table 2, the best performance occurs at the first few iterations.


Reisinger and Mooney (2010b) introduced a multi-prototype VSM where word sense discrimination is first applied by clustering contexts, and then prototypes are built using the contexts of the sense-labeled words.

in order to cluster accurately, it is important to capture both the syntax and semantics of words.


We evaluate our new model on the standard WordSim-353 (Finkelstein et al., 2001) dataset that includes human similarity judgments on pairs of words, showing that combining both local and global context outperforms using only local or global context alone, and is competitive with stateof-the-art methods.

one limitation of this evaluation is that the human judgments are on pairs  Figure 1: An overview of our neural language model.


We used 5,000 iterations because this is the software's default setting; evaluating the trace output suggests it only takes several hundred iterations to "burn in".

we ran 8 chains for 25,000 iterations of the colloc model; as expected the results of this run are within two standard deviations of the results reported above.


Psychological proposals have suggested that children may discover that particular social cues help in establishing reference (Baldwin, 1993;Hollich et al., 2000), but prior modeling work has often assumed that cues, cue weights, or both are prespecified.

the models described here could in principle discover a wide range of different social conventions.


At the root node trying, CLN(trying) is 6 because there are six crossing-links under its sub-span: (e 1 j 4 , e 2 j 3 ), (e 1 j 4 , e 4 j 2 ), (e 1 j 4 , e 5 j 1 ), (e 2 j 3 , e 4 j 2 ), (e 2 j 3 , e 5 j 1 ) and (e 4 j 2 , e 5 j 1 ).

ccLN(trying) is 5 because (e 4 j 2 , e 5 j 1 ) falls under its child node play, thus does not count towards ccLN of trying.


our ranking reordering model indeed significantly reduces the crossing-link numbers over the original sentence pairs.

the performance of the ranking reorder model still fall far short of oracle, which is the lowest crossing-link number of all possible permutations allowed by the parse tree.


But with phrase matching in Chinese, it must be modeled explicitly.

we cannot simply perform covered ngram matching as a post processing step.


It has been shown to give better correlations than BLEU for many European languages including English (Callison-Burch et al., 2011).

its use of POS tags and synonym dictionaries prevents its use at the character-level.


Some sample sentences taken from the IWSLT test set are shown in Table 4 (some are simplified from the original).

the Cilin dictionary correctly identified the following as synonyms: the dictionary fails to recognize the following synonyms: 一个 = 个 a 这儿 = 这里 here partial awards are still given for the matching characters 这 and 个.


In the current formulation of TESLA-CELAB, two n-grams X and Y are either synonyms which completely match each other, or are completely unrelated.

the linear-programming based TESLA metric allows fractional similarity measures between 0 (completely unrelated) and 1 (exact synonyms).


We introduce a function m(X) that assigns a weight in [0, 1] for each n-gram X.

accordingly, our objective function is replaced by: where Z is a normalizing constant so that the metric has a range of [0,1] experiments with different weight functions m(•) on the test data set failed to find a better weight function than the currently implied m(•) = 1.


This is probably due to the linguistic characteristics of Chinese, where human judges apparently give equal importance to function words and content words.

tESLA-M found discounting function words very effective for English and other European languages such as German.


(2011) claimed that TESLA tuning performed better than BLEU tuning according to human judgment.

in the WMT 2011 "tunable metrics" shared pilot task, this did not hold (Callison-Burch et al., 2011).


This metric is similar to Spearman's ρ (Spearman, 1904).

we have found that ρ punishes long-distance reorderings too heavily.


In statistical learning theory, it is assumed that the training and test datasets are drawn from the same distribution, or in other words, they are from the same domain.

bilingual corpora are only available in very limited domains and building bilingual resources in a new domain is usually very expensive.


So the list of rules coming from each model for a cell in CKY chart is normalized before getting mixed with other phrase-table rules.

experiments showed changing the scores with the normalized scores hurts the BLEU score radically.


The feature set of these hypotheses are expanded to include one feature set for each table.

for the corresponding feature values of those phrase-tables that did not have a particular phrase-pair, a default log probability value of 0 is assumed (Bertoldi and Federico, 2009) which is counter-intuitive as it boosts the score of hypotheses with phrase-pairs that do not belong to all of the translation tables.


Besides, this model is formal syntax-based and does not need to specify the syntactic constituents of subphrases, so it can directly learn synchronous context-free grammars (SCFG) from a parallel text without relying on any linguistic annotations or assumptions, which makes it used conveniently and widely.

it is often desirable to consider syntactic constituents of subphrases, e.g.


Unlike some of the discriminative baselines, which require expensive operations 9 on 9 It is true that in order train our system, one must parse large amounts of training data, which can be costly, though it only needs to be done once.

even with observed training trees, the discriminative algorithms must still iteratively perform expensive operations (like parsing) for each sentence, and a new model must be trained for new types of negative data.


We see that our system prefers the reference much more often than the 5-GRAM language model.

11 we also note that the easiness of the task is correlated with the quality of translations (as measured in BLEU score).


Moreover, the authors only tested for the specific language pair of English embedded in German texts.

our work considers more than 200 languages, and the portions of embedded text are larger: up to the paragraph level to accommodate the reality of multilingual texts.


The translation quality of the SMT system is highly related to the coverage of translation models.

no matter how much data is used for training, it is still impossible to completely cover the unlimited input sentences.


Take the sentence pair in Figure 2 as an example, two initial phrase pairs PP 1 = "那 只 蓝色 手提包 ||| 那 个 蓝色 手提包" and PP 2 = "对 那 只 蓝色 手提包 有 兴趣 ||| 很 感 兴趣 那 个 蓝色 手提包" are identified, and PP 1 is contained by PP 2 , then we could form the rule: 对 X 1 有 兴趣  很 感 兴趣 X 1 to have interest very feel interest The extracted paraphrase rules aim to rewrite the input sentences to an MT-favored form which may lead to a better translation.

it is risky to directly replace the input sentence with a paraphrased sentence, since the errors in automatic paraphrase substitution may jeopardize the translation result seriously.


Both tried to capture the MT-favored structures from bilingual corpus.


EEM requires 10GB of memory and cannot handle words with more than 200,000 MDSs: for UF we left the SAT solver running for a week without ever terminating.

it takes about 4 hours if we limit the set Figure 1: Human classification of (in)consistent words.


de Oliveira (2011) modeled the similarity between the model and candidate summaries as a maximum bipartite matching problem, where the two summaries are represented as two sets of nodes and precision and recall are cal-  culated from the matched edges.

none of the AESOP metrics currently apply deep linguistic analysis, which includes discourse analysis.


observed that coherent texts preferentially follow certain relation patterns.

simply using such patterns to measure the coherence of a text can result in feature sparseness.


Most previous studies on POS tagging have focused on how to extract more linguistic features or how to adopt supervised or unsupervised approaches based on a single evaluation measure, accuracy.

with a different viewpoint for errors on POS tagging, there is still some room to improve the performance of POS tagging for subsequent NLP tasks, even though the overall accuracy can not be much improved.


The loss function L c (y i , y j ) is designed to reflect the categories in Table 1.

the structure of POS tags can be represented as a more complex structure.


The feature set of our model is fundamentally a combination of the features used in the state-of-the-art joint segmentation and POS tagging model (Zhang and Clark, 2010) and dependency parser (Huang and Sagae, 2010), both of which are used as baseline models in our experiment.

we must carefully adjust which features are to be activated and when, and how they are combined with which action labels, depending on the type of the features because we intend to perform three tasks in a single incremental framework.


Irrespective of the existence of the dictionary features, the joint model SegTagDep largely increases the POS tagging and dependency parsing accuracies (by 0.56-0.63% and 2.34-2.44%); the improvements in parsing accuracies are still significant even compared with SegTag+Dep' (the pipeline model with the look-ahead features).

when the external dictionaries are not used ("wo/dict"), no substantial improvements for segmentation accuracies were observed.


However, when the external dictionaries are not used ("wo/dict"), no substantial improvements for segmentation accuracies were observed.

when the dictionaries are used ("w/dict"), the segmentation accuracies are now improved over the baseline model SegTag consistently (on every trial).


The partially joint model SegTag+TagDep is shown to perform reasonably well in dependency parsing: with dictionaries, it achieved the 2.02% improvement over SegTag+Dep, which is only 0.32% lower than SegTagDep.

whereas Seg-Tag+TagDep showed no substantial improvement in tagging accuracies over SegTag (when the dictionaries are used), SegTagDep achieved consistent improvements of 0.46% and 0.58% (without/with dic-   tionaries); these differences can be attributed to the combination of the relieved error propagation and the incorporation of the syntactic dependencies.


Dynamic programming techniques based on Markov assumptions, such as Viterbi decoding, cannot handle those 'non-local' constraints as discussed above.

it is possible to constrain Viterbi decoding by 'local' constraints, e.g.


2 English POS tagging 2.1 Explore deterministic constraints Suppose that, following (Chomsky, 1970), we distinguish major lexical categories (Noun, Verb, Adjective and Preposition) by two binary features: A word occurring in between a preceding word the and a following word of always bears the feature +N.

consider the annotation guideline of English Treebank (Marcus et al., 1993) instead.


Viterbi algorithm is widely used for tagging, and runs in O(nT 2 ) when searching in an unconstrained space.

consider searching in a constrained space.


For example, after decoding with BMES, 4 consecutive characters associated with the tag sequence BMME compose a word.

after decoding with IB, characters associated with BIII may compose a word if the following tag is B or only form part of a word if the following tag is I.


As shown in Table 8, when tagset IB is used for character tagging, high precision predictions can be made by the deterministic constraints that are learned with respect to this tagset.

when tagset BMES is used, the learned constraints don't always make reliable predictions, and the overall precision is not high enough to constrain a probabilistic model.


As shown in Table 9, when the beam-width is reduced from 5 to 1, the tagger (beam=1) is 3 times faster but tagging accuracy is badly hurt.

when searching in a constrained space rather than the raw space, the constrained tagger (beam=5) is 10 times fast as the baseline and the tagging accuracy is even moderately improved, increasing to 97.20%.


Following Huang (2008), this algorithm traverses a parse forest in a bottom-up manner.

it determines and keeps the best derivation for every grammar rule instance instead of for each node.


This algorithm is more complex than the approximate decoding algorithm of Huang (2008).

its efficiency heavily depends on the size of the parse forest it has to handle.


Therefore, the ratio of constituents to distituents is not constant across sentence lengths.

by virtue of the log-linear model, LLCCM assigns positive probability to all spans or contexts without explicit smoothing.


Performance of the monolingual MT-based method in paraphrase generation is limited by the large-scale paraphrase corpus it relies on as the corpus is not readily available .

bilingual parallel data is in abundance and has been used in extracting paraphrase (Bannard and Callison-Burch, 2005;Zhao et al., 2008b;Callison-Burch, 2008;Kok and Brockett, 2010;Kuhn et al., 2010;Ganitkevitch et al., 2011).


The most related work to ours is the boostVSM introduced by (He et al., 2007b), it proposes to weight different term dimensions with corresponding bursty scores.

it is still based on term dimensions and fails to deal with terms with multiple bursts.


Some methods try to design time decaying functions (Yang et al., 1998), which decay the similarity with the increasing of time gap between two documents.

it requires efforts for function selection and parameters tuning.


It is possible that a discussant may be replying to another poster but expressing an attitude towards a third entity or discussant.

as a simplifying assumption, similar to the work of (Hassan et al., 2010), we adopt the view that replies in the sentences that are determined to be attitudinal and contain secondperson pronouns (you, your, yourself) are assumed to be directed towards the recipients of the replies.


The letter-frequency distribution of running key ciphertexts is notably flatter than than the plaintext distribution, unlike substitution ciphers where the frequency profile remains unchanged, modulo letter substitutions.

the ciphertext letter distribution is not uniform; there are peaks corresponding to letters (like I) that are formed by high-frequency plaintext/key pairs (like E and E).


While (Ng and Jordan, 2002) showed that NB is better than SVM/logistic regression (LR) with few training cases, we show that MNB is also better with short documents.

to their result that an SVM usually beats NB when it has more than 30-50 training cases, we show that MNB is still better on snippets even with relatively large training sets (9k cases).


This likely reflects that certain topic keywords are indicative alone.

in both tables 2 and 3, adding bigrams always improved the performance, and often gives better results than previously published.


However, in both tables 2 and 3, adding bigrams always improved the performance, and often gives better results than previously published.

8 This presumably reflects that in sentiment classification there are 8 adding trigrams hurts slightly.


Another way is to evaluate only on the 51% of sentences for which our conversion from gold CCG derivations is perfect (CLEAN).

even on this set our conversion introduces errors, as the parser output may contain categories that are harder to convert.


As shown earlier, CLEAN does not completely factor out the errors introduced by our conversion, as the parser output may be more difficult to convert, and the calculation of PROJ only roughly factors out the errors.

the results do suggest that the performance of the CCG parsers is approaching that of the Petrov parser.


From a practical point of view, we show that an induced TIG provides modeling performance superior to TSG and comparable with TIG 0 .

we show that the grammars we induce are compact yet rich, in that they succinctly represent complex linguistic structures.


For example, a sentence either comes from newswire, or weblog, but not both.


"NO" entailment in both directions) are not present in the annotation.

this is the only available dataset suitable to gather insights about the viability of our approach to multi-directional CLTE recognition.


These approaches align at the syntactic level (using CFGs and dependencies respectively).

to the above approaches, we assume the existence of grammars and use a semantic representation as the appropriate level for cross-lingual processing.


Our system thus depends on a range of existing technologies.

these technologies are available for a range of languages, and we use them for efficient extension of linguistic resources.


They use heuristics to align words and translations, while we use a learning based approach to find translations.

to previous work described above, we exploit surface patterns differently as a soft constraint, while requiring minimal human intervention to prepare the training data.


The definitions are extended by definitions of neighbor senses to discover more overlapping words.


Accordingly we are interested in extracting latent semantics from sense definitions to improve elesk.

the challenge lies in that sense definitions are typically too short/sparse for latent variable models to learn accurate semantics, since these models are designed for long documents.


Grenager and Manning (2006) use an ordering of the linking of semantic roles and syntactic relations.

as the space of possible linkings is large, language-specific knowledge is used to constrain this space.


The importance of inference rules to semantic applications has long been recognized and extensive work has been carried out to automatically acquire inference-rule resources.

evaluating such resources has turned out to be a non-trivial task, slowing progress in the field.


A major limitation of the list compiled by Shetty and Adibi (2004) is that it only covers those "core" employees for whom the complete email inboxes are available in the Enron dataset.

it is also interesting to determine whether we can predict the hierarchy of other employees, for whom we only have an incomplete set of emails (those that they sent to or received from the core employees).


Natural language questions have become popular in web search.

various questions can be formulated to convey the same information need, which poses a great challenge to search systems.


Indeed, Wong and Dras (2011a) claims that Information Gain is a better criteria.

this metric requires a probabilistic formulation of the grammar, which 2DOP does not supply.


As the number of learners of English is constantly growing, automatic error correction of ESL learners' writing is an increasingly active area of research.

most research has mainly focused on errors concerning articles and prepositions even though tense/aspect errors are also important.


Of these our system detected 61 and successfully corrected 52 instances.

of the second most frequent error type (using simple past instead of simple present), with 94 instances in the corpus, our system only detected 9 instances.


Blogs and forums are widely adopted by online communities to debate about various issues.

a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions, and can be discouraged from interacting through these applications.


In such applications, users are asked to provide their own opinions about selected issues.

it may happen that the debates become rather complicated, with several arguments supporting and contradicting each others.


Their approach can thus be applied to comparably small data sets.

they are restricted to a specific type of relations whereas here the entire bandwidth of discourse relations that are explicitly realized in a language are covered.


Similar observations can be made with respect to Chambers and Jurafsky (2009) and Kasch and Oates (2010), who also study a single discourse relation (narration), and are thus more limited in scope than the approach described here.

as their approach extends beyond pairs of events to complex event chains, it seems that both approaches provide complementary types of information and their results could also be combined in a fruitful way to achieve a more detailed assessment of discourse relations.


Between these fine and coarse-grained approaches, event identification requires grouping references to the same event.

strict coreference is hampered by the complexity of event semantics: poison, murder and die may indicate the same effective event.


or "Who are the teammates of Lionel Messi at FC Barcelona?".

factual knowledge is highly ephemeral: Royals get married and divorced, politicians hold positions only for a limited time and soccer players transfer from one club to another.


The reason is, that the types of worksForClub distinguish the patterns well from other relations.

isMar-riedTo's patterns interfere with other person-person relations making constraints a decisive asset.


That is because the joint model's ILP decides with binary variables on which patterns to accept.

label propagation addresses the inherent uncertainty by providing label assignments with confidence numbers.


This is the setting usual pattern-based approaches without cleaning stage are working in.

for the standard setting (coinciding with Table 1's left column) stage 3 yields less precision, but higher recall.


Overall, these tests suggest that in general, the 'use-everything' approach is better for accurate classification of Hittite tablet fragments with larger CTH texts.

in some cases, when the fragments in question have a large number of Sumerograms and Akkadograms, using them exclusively may be the right choice.


lDA does not model authors explicitly, and we are not aware of any previous studies that apply author-aware topic models to traditional AA.


Existing methods were designed for data from single domain, assuming that either view alone is sufficient to predict the target class accurately.

this view-consistency assumption is largely violated in the setting of domain adaptation where training and test data are drawn from different distributions.


Topic modeling with a tree-based prior has been used for a variety of applications because it can encode correlations between words that traditional topic modeling cannot.

its expressive power comes at the cost of more complicated inference.


This is not a problem for SparseLDA because s is shared across all tokens.

we can achieve computational gains with an upper bound on s, A sampling algorithm can take advantage of this by not explicitly calculating s. Instead, we use s as proxy, and only compute the exact s if we hit the bucket s (Algorithm 1).


These represent reasonable disambiguations.

to previous approaches, inference speeds up as topics become more semantically coherent (Boyd-Graber et al., 2007).


More than 70% of the unique tokens appear less than 5 times in WCorpus.

over half of the tokens appear more than or equal to 5 times in the CCorpus.


As character could preserve more meanings than word in Chinese, it seems that a character can be wrongly aligned to many English words by the aligner.

we found this can be avoided to a great extent by the basic features (co-occurrence and distortion) used by many alignment models.


Statistical machine translation (SMT) systems, require parallel corpora of sentences and their translations, called bitexts, which are often not sufficiently large.

for many closely-related languages, SMT can be carried out even with small bitexts by exploring relations below the word level.


, q e t o $ i Certainly, translation cannot be adequately modeled as simple transliteration, even for closelyrelated languages.

the strength of phrasebased SMT (Koehn et al., 2003) is that it can support rather large sequences (phrases) that capture translations of entire chunks.


Sentence pair count e 2 9 f 3 e 2 2 f 2 e 1 e 2 1 f 1 f 2 In this way, VB controls the overfitting that would otherwise occur with rare words.

higher values of α can be chosen if smoothing is desired, for instance in the case of the alignment probabilities, which state how likely a word in position i of the English sentence is to align to a word in position j of the French sentence.


The language model was a trigram model with modified Kneser-Ney smoothing (Kneser and Ney, 1995;Chen and Goodman, 1998), trained on the target  Table 2: Forest-to-string translation outperforms tree-tostring translation according to Bleu, but the decreases parsing accuracy according to labeled-bracket F1.

when we train to maximize labeled-bracket F1, forest-to-string translation yields better parses than both tree-to-string translation and the original parser.


The MER trainer requires that each list contain enough unique translations (when maximizing Bleu) or source trees (when maximizing labeled-bracket F1).

because one source tree may lead to many translation derivations, the n-best list may contain only a few unique source trees, or in the extreme case, the derivations may all have the same source tree.


This might result from the phenomenon alluded to in Section 4, where additional data sometimes degrades performance for unsupervised analyzers.

the Lee segmenter's gain on Levantine (18%) is higher than its gain on Small MSA (13%), even though Levantine has more data (1.5M vs. 1.3M words).


For example, Dahlmeier and Ng (2011) proposed a method that combines a native corpus and a GE tagged learner corpus and it outperformed models trained with either a native or GE tagged learner corpus alone.

methods which train a GEC model from various GE tagged corpora have received less focus.


Convolution kernels support the modeling of complex syntactic information in machinelearning tasks.

such models are highly sensitive to the type and size of syntactic structure used.


Recently, researchers started to exploit content information in data-driven diffusion models Petrovic et al., 2011;Zhu et al., 2011).

most of the data-driven approaches assume that in order to train a model and predict the future diffusion of a topic, it is required to obtain historical records about how this topic has propagated in a social network (Petrovic et al., 2011;Zhu et al., 2011).


Consequently, they need to rely on a model-driven approach instead of a datadriven approach.

our work focuses on the prediction of explicit diffusion behaviors.


Labels A0, A1, and A2 are complement case roles and over 85% of them survive with their predicates.

for modifier arguments (AM-X), survival ratios are down to lower than 65%.


We found that summarization system ranking, based on scores for multiple topics, was surprisingly stable and didn't change signifi-  cantly when several topics were removed from consideration.

on a summary level, removing topics scored by the most inconsistent assessors helped ROUGE-2 increase its correlation with human metrics.


The work on morphologically rich languages suggests that using comprehensive morphological dictionaries is necessary for achieving good results (Hajič, 2000;Erjavec and Džeroski, 2004).

such dictionaries are constructed manually and they cannot be expected to be developed quickly for many languages.


Position of error within a word ( Figure 5) In en keystroke, Deletion errors at the word-initial position are the most common, while Insertion and Substitution errors tend to occur both at the beginning and the end of a word.

in en common, all error types are more prone to occur word-medially.


Our overall results are comparable to what Huang and Zhao (2007) report.

the consistency is quickly falling for longer words: on unigrams, f-scores range from 0.81 to 0.90 (the same as the overall results).


One challenge is that MST parsing itself is not incremental, making it expensive to identify loops during hypothesis expansion.

shiftreduce parsing is naturally incremental and can be seamlessly integrated into left-to-right phrasebased decoding.


Therefore, the possible improvements resulted from those pipeline approaches are quite limited.

instead of directly merging TM matched phrases into the source sentence, some approaches (Biç ici and Dymetman, 2008;Simard and Isabelle, 2009) simply add the longest matched pairs into SMT phrase table, and then associate them with a fixed large probability value to favor the corresponding TM target phrase at SMT decoding.


On the other hand, instead of directly merging TM matched phrases into the source sentence, some approaches (Biç ici and Dymetman, 2008;Simard and Isabelle, 2009) simply add the longest matched pairs into SMT phrase table, and then associate them with a fixed large probability value to favor the corresponding TM target phrase at SMT decoding.

since only one aligned target phrase will be added for each matched source phrase, they share most drawbacks with the pipeline approaches mentioned above and merely achieve similar performance.


This is because the features adopted in their discriminative learning are complicated and difficult to re-implement.

the proposed Model-III even outperforms the upper bound of their methods, which will be discussed later.


It can be seen that "you do" is redundant for Koehn-10, because they are insertions and thus are kept in the XML input.

sMT system still inserts another "you", regardless of "you do" has already existed.


Moreover, following the approaches of Koehn-10 and Ma-11 (to give a fair comparison), training data for SMT and TM are the same in the current experiments.

the TM is expected to play an even more important role when the SMT training-set differs from the TM database, as additional phrase-pairs that are unseen in the SMT phrase table can be extracted from TM (which can then be dynamically added into the SMT phrase table at decoding time).


The multi-task learning methods performed best when using the annotator identity as the task descriptor, and less well for the MT system and sentence pair, where they only slightly improved over the baseline.

making use of all these layers of metadata together gives substantial further improvements, reaching the best result with Combined A,S,T .


On the other hand, there has been research on training object and event models from large corpora of complex images and video in the computer-vision community (Kuznetsova et al., 2012;Sadanand and Corso, 2012;Ordonez et al., 2011;Yao et al., 2010).

most such work requires training data that labels individual concepts with individual words (i.e., ob-jects delineated via bounding boxes in images as nouns and events that occur in short video clips as verbs).


Previous work (e.g., Morante and Blanco, 2012) has investigated automatically detecting the scope and focus of negation.

the scope of negation with respect to quantifiers is a different phenomenon.


Manshadi and Allen (2011a), hence MA11, go beyond those limitations and scope an arbitrary number of NPs in a sentence with no restriction on the type of quantification.

although their corpus annotates the scope of negations and the implicit universal of plurals, their QSD system does not handle those.


Many ranking systems create partial orders as output when the confidence level for the relative order of two objects is below some threshold.

the target being a partial order is a fundamentally different problem.


From these examples, as long as we create two nodes in the DAG corresponding to each plural chunk, and one node corresponding to each negation, there is no need to modify the underlying model (defined in the previous section).

when u (or v) is a negation (N i) or an implicit universal (id) node, the probabilities p λ u,v (λ ∈ {+, −, }) may come from a different source, e.g.


in order to have a fair comparison, we have used the output of the Stanford parser to automatically generate the same features that MA11 have hand-annotated.


Because of the ambiguity, a local classifier may miss it or mislabel it as a trigger of End-Position.

knowing that "tank" is very likely to be an Instrument argument of Attack events, the correct event subtype assignment of "fired" is obviously Attack.


arbitrary global features over multiple local predictions.

different from easier tasks such as part-of-speech tagging or noun phrase chunking where efficient dynamic programming decoding is feasible, here exact joint inference is intractable.


Assigning labels based on thread boundaries allows for context to be meaningfully taken into account, without crossing topic boundaries.

this granularity comes with a price: the distribution of class values in these instances is highly skewed.


Exact inference is intractable due to the E factors that couple all of the a i by way of the p i nodes.

we can compute approximate marginals for the a i , p i , and r i using belief propagation.


For example, Figure 1(b) shows the structure of the word "建筑业 (construction and building industry)", where the characters "建 (construction)" and "筑 (building)" form a coordination, and modify the character "业 (industry)".

computational processing of Chinese is typically based on words.


With these methods, transition-based parsers have reached state-of-the-art accuracy for a number of languages (Zhang and Nivre, 2011;Bohnet and Nivre, 2012).

the drawback with this approach is that parsing speed is proportional to the size of the beam, which means that the most accurate transition-based parsers are not nearly as fast as the original greedy transition-based parsers.


This is bad, because the information about the correct attachment could come from the lexical content of node P. The arc-eager model performs slightly better, since it can delay the decision up to the point in which α 1 has been constructed and P is read from the buffer.

at this point it must make a commitment and either construct α 3 or pop N1 from the stack (implicitly committing to α 2 ) before N2 is read from the buffer.


However, at this point it must make a commitment and either construct α 3 or pop N1 from the stack (implicitly committing to α 2 ) before N2 is read from the buffer.

with this scenario, in the next sections we implement a dynamic parsing strategy that allows a transition system to decide between the attachments α 2 and α 3 after it has seen all of the four nodes V, N1, P and N2.


More specifically, (i) generalizes the la/ra transitions to the la k /ra k transitions, introducing a top-down component into the purely bottom-up arc-standard.

(ii) drops the limitation of canonical computations for the arc-standard, and leverages on the spurious ambiguity of the parser to enlarge the search space.


In (d) and (e) in Figure 2, the word (kare) at the CP and the word order between katta and karita are the same.

the word at the NP for (d) and the word at the NP for (e) are different.


In the pair model, the position pair of (i, j) is used to derive features.

to descriminate label sequences in the sequence model, the position pairs of (i, k), k ∈ {k|i < k ≤ j ∨ j ≤ k < i} and (k, j), k ∈ {k|i ≤ k < j ∨ j < k ≤ i} are used to derive features.


Figure 5 shows that when a distance class feature used in the model was the same (e.g., distortions from 5 to 20 were the same distance class feature), PAIR produced average distortion probabilities that were almost the same.

the average distortion probabilities for SEQUENCE decreased when the lengths of the distortions increased, even if the distance class feature was the same, and this behavior was the same as that of CORPUS.


From CORPUS, the average probabilities in the training data for each distortion in [4, 6] were higher than those for each distortion in [7,20].

the converse was true for the comparison between the two average probabilities for the outbound model.


As a matter of fact, even without any contexts, the lexical translation table in HMM already contains O(|V e | * |V f |) parameters, where |V e | and V f denote source and target vocabulary sizes.

our model does not maintain a separate translation score parameters for every source-target word pair, but computes t lex through a multi-layer network, which naturally handles contexts on both sides without explosive growth of number of parameters.


The drawback is that there is a margin of error in the parallel segment identification and alignment.

our system can be tuned for precision or for recall.


This is to be expected, since our dataset was extracted from an extremely different domain.

by combining the Weibo parallel data with this standard data, improvements in BLEU are obtained.


The fact that gLDA+SVM performs better than the standard gSLDA is due to the same reason, since the SVM part of gL-DA+SVM can well capture the supervision information to learn a classifier for good prediction, while standard sLDA can't well-balance the influence of supervision.

the well-balanced gSLDA+ model successfully outperforms the twostage approach, gLDA+SVM, by performing topic discovery and prediction jointly 4 .


In our initial modified version, we replaced the gold-standard reference parse with the pseudo-gold reference, which has the highest execution rate amongst all candidate parses.

this ignores all other candidate parses during perceptron training.


However, this ignores all other candidate parses during perceptron training.

it is not ideal to regard other candidate parses as "useless."


The results show that our response-based approach (Single) has better execution accuracy than both the baseline and the standard approach using gold-standard parses (Gold).

gold does perform best on parse accuracy since it explicitly focuses on maximizing the accuracy of the resulting MR.


However, Gold does perform best on parse accuracy since it explicitly focuses on maximizing the accuracy of the resulting MR.

by focusing discriminative training on optimizing performance of the ultimate end task, our response-based approach actually outperforms the traditional approach on the final task.


For our task evaluation, ideally, we would like the system to be able to identify the news article specifically referred to by the url within each tweet in the gold standard.

this is very difficult given the large number of potential candidates, especially those with slight variations.


Our paper resembles it in searching for a related news article.

we target on recommending news article only based on a tweet, which is a much smaller context than the set of favorite documents chosen by a user .


5 Binary perception Although we did not impose a discrete categorization of politeness, we acknowledge an implicit binary perception of the phenomenon: whenever an annotator moved a slider in one direction or the other, she made a binary politeness judgment.

the bound- Quartile: 1 st 2 nd 3 rd 4 th Wiki 62% 8% 3% 51% SE 37% 4% 6% 46% ary between somewhat polite and somewhat impolite requests can be blurry.


The previous section does not test this hypothesis, since all editors compared in Table 5 had the same (non-admin) status when writing the requests.

our data does provide three ways of testing this hypothesis.


Politeness marking is one aspect of the broader issue of how language relates to power and status, which has been studied in the context of workplace discourse (Bramsen et al., ;Diehl et al., 2007;Peterson et al., 2011;Prabhakaran et al., 2012;Gilbert, 2012;McCallum et al., 2007) and social networking (Scholand et al., 2010).

this research focusses on domain-specific textual cues, whereas the present work seeks to leverage domain-independent politeness cues, building on the literature on how politeness affects worksplace social dynamics and power structures (Gyasi Obeng, 1997;Chilton, 1990;Andersson and Pearson, 1999;Rogers and Lee-Wong, 2003;Holmes and Stubbe, 2005).


Let tp i be the number of test essays correctly labeled as positive by error e i 's binary classifier b i ; p i be the total number of test essays labeled as positive by b i ; and g i be the total number of test essays that belong to e i according to the gold standard.

then, the precision (P i ), recall (R i ), and F-score (F i ) for b i and the macro F-score (F) of the combined system for one test fold are calculated by the macro F-score calculation can be seen as giving too much weight to the less frequent errors.


Similarly, the word "discussions", which is the last word in this sequence, cannot have any right children and we can estimate that its right P stop probability is high.

non-reducible words such, as the verb "asked" in our example, can have children, and therefore their P stop can be estimated as low for both directions.


If we use a corpus containing about 10,000 sentences, it is possible that we found no reducible sequences at all.

we managed to find a sufficient amount of reducible sequences in corpora containing millions of sentences, see Section 6.1 and Table 1.


The quality of such tagging is not very high since we do not use any lexicons or pretrained models.

it is sufficient for obtaining usable stop probability estimates.


All of these formalisms share a similar basic syntactic structure with Penn Treebank CFG.

the target formalisms also encode additional constraints and semantic features.


As such, probabilistic modeling for TAG in its original form is uncommon.

a large effort in non-probabilistic grammar induction has been performed through manual annotation with the XTAG project (Doran et al., 1994).


These rules highlight the linguistic intuitions that back TAG; if their adjunction were undone, the remaining derivation would be a valid sentence that simply lacks the modifying structure of the auxiliary tree.

the MPD parses reveal that not all useful adjunctions conform to this paradigm, and that left-auxiliary trees that are not used for sister adjunction are susceptible to this behavior by adjoining the shared unbracketed syntax onto the NP dominating the bracketed text.


Adaptation of discriminative learning methods for these types of features to statistical machine translation (MT) systems, which have historically used idiosyncratic learning techniques for a few dense features, has been an active research area for the past half-decade.

despite some research successes, feature-rich models are rarely used in annual MT evaluations.


Crucially, it does not learn the basic rule → program.

the bitext5k model contains basic rules such → programme, → this programme, and → that programme.


In this way, syntax information can be incorporated into phrase-based SMT systems.

one disadvantage is that the reliability of the rules is often language pair dependent.


For the first method, we adopt the linear-chain CRFs.

even for the simple linear-chain CRFs, the complexity of learning and inference grows quadratically with respect to the number of output labels and the amount of structural features which are with regard to adjacent pairs of labels.


Most modern machine translation systems use phrase pairs as translation units, allowing for accurate modelling of phraseinternal translation and reordering.

phrase-based approaches are much less able to model sentence level effects between different phrase-pairs.


On one hand, the use of phrases can memorize local context and hence helps to generate better translation compared to word-based models (Brown et al., 1993;Och and Ney, 2003).

this mechanism requires each phrase to be matched strictly and to be used as a whole, which precludes the use of discontinuous phrases and leads to poor generalisation to unseen data (where large phrases tend not to match).


Our work also uses bilingual information, using the source words as part of the conditioning context.

to these approaches which primarily address the decoding problem, we focus on the learning problem of inferring alignments from parallel sentences.


<烧 烤 类 型 的, grill-type>, for "家" and "点 的" are wrongly aligned to "grill-type").

our model better aligns the function words, such that many more useful phrase pairs can be extracted, i.e., <在, 'm>, <找, looking for>, <烧烤 类型, grill-type> and their combinations with neighbouring phrase pairs.


MNB uses add-1 smoothing to estimate the conditional probability of the word "resources" in each class as θ + w = 1+1 216+33504 = 5.93e-5, and implying that "resources" is a negative indicator of the Earnings class.


SFE has the same scalability advantages as MNB-FM.

unlike our approach, SFE does not compute maximumlikelihood estimates using the marginal statistics as a constraint.


But obtaining parallel data is an expensive process and not available for all language pairs or domains.

monolingual data (in written form) exists and is easier to obtain for many languages.


The MT literature does cover some prior work on extracting or augmenting partial lexicons using non-parallel corpora (Rapp, 1995;Fung and McKeown, 1997;Koehn and Knight, 2000;Haghighi et al., 2008).

none of these methods attempt to train end-to-end MT models, instead they focus on mining bilingual lexicons from monolingual corpora and often they require parallel seed lexicons as a starting point.


(2010) presented algorithms and data structures that allow number-range queries for searching documents.

these studies do not interpret the quantity (e.g., 3,000,000,000) of a numerical expression (e.g., 3b people), but rather treat numerical expressions as strings.


(2007) and Davidov and Rappoport (2010) rely on hand-crafted patterns (e.g., "Object is * [unit] tall"), focusing on a specific set of numerical attributes (e.g., height, weight, size).

this study can handle any kind of target and situation that is quantified by numbers, e.g., number of people facing a water shortage.


(2011) designed hand-crafted rules for matching intervals expressed by temporal expressions.

these studies do not necessarily focus on semantic processing of numerical expressions; thus, these studies do not normalize units of numerical expressions nor make inferences with numerical common sense.


The underlying assumption of this approach is that the real distribution of a query (e.g., money given to a friend) can be approximated by the distribution of numbers co-occurring with the context (e.g., give and friend) on the Web.

the context space generated in Section 4.2 may be too sparse to find numbers in the database, especially when a query context is fine-grained.


Generative probabilistic models have been used for content modelling and template induction, and are typically trained on small corpora in the target domain.

vector space models of distributional semantics are trained on large corpora, but are typically applied to domaingeneral lexical disambiguation tasks.


Thus, the only way these terms could be recognized as positive is if they are found in the GIZA++ dictionaries.

due to data sparsity in these dictionaries this did not happen in these cases.


A plausible reason for such a performance improvement is the reduction in data sparsity.

such a reduction could be achieved with a lesser effort through the means of syntagma based word clustering.


WordNets are primarily used to address the problem of word sense disambiguation.

at present there are many NLP applications which use WordNet.


This might have had a degrading effect on the SA accuracy.

it was seen that classifier developed on cluster features based on syntagmatic analysis do not suffer from this.


The approach presented here for CLSA will still require a parallel corpora.

the size of the parallel corpora required for CLSA can considerably be much lesser than the size of the parallel corpora required to train an MT system.


Unlike a classifier, MATCHER does not output any single matching M .

downstream applications can easily convert MATCHER's output into a matching M by, for instance, selecting the top K candidate r T values for each r D , or by selecting all (r T , r D ) pairs with a score over a chosen threshold.


Hence, the CVG builds on top of a standard PCFG parser.

many parsing decisions show fine-grained semantic factors at work.


Note that any PCFG, including latent annotation PCFGs (Matsuzaki et al., 2005) could be used.

since the vectors will capture lexical and semantic information, even simple base PCFGs can be substantially improved.


For dialog state tracking, most commercial systems use hand-crafted heuristics, selecting the SLU result with the highest confidence score, and discarding alternatives.

statistical approaches compute a posterior distribution over many hypotheses for the dialog state.


The model is based on M + K feature functions.

unlike in traditional maximum entropy models such as the fixed-position model above, these features functions are dynamically defined when presented with each turn.


Recently developed statistical approaches are promising as they fully utilize the dialog history, and can incorporate priors from past usage data.

existing methodologies are either limited in their accuracy or their coverage, both of which hamper performance.


Furthermore, (Blair-Goldensohn, 2007) improved previous work with the use of parameter optimization, topic segmentation and syntactic parsing.

(Sporleder and Lascarides, 2008) showed that the training model built on a synthetic data set, like the work of (Marcu and Echihabi, 2002), may not be a good strategy since the linguistic dissimilarity between explicit and implicit data may hurt the performance of a model on natural data when being trained on synthetic data.


Very recently, (Hernault et al., 2011) introduced a semi-supervised work using structure learning method for discourse relation classification, which is quite relevant to our work.

they performed discourse relation classification on both explicit and implicit data.


On one hand, since building a hand-annotated implicit discourse relation corpus is costly and time consuming, most previous work attempted to use synthetic implicit discourse examples as training data.

(Sporleder and Lascarides, 2008) found that the model trained on synthetic implicit data has not performed as well as expected in natural implicit data.


This indicates that straightly using synthetic implicit data as training data may not be helpful.

as shown in Section 1, we observe that in some cases explicit discourse relation and implicit discourse relation can express the same meaning with or without a discourse connective.


we could use forwards-backwards algorithm for exact inference in this model (Sutton and McCallum, 2012).

forwards-backwards on a sequence containing T units costs where M is the number of relations in our relation set.


Each node is seen two times so the time complexity is linear in the number of nodes which is at least O(2 n ).

only nodes that have encountered at least one training instance are useful and there are O(n × k) such nodes (where k the size of the training set).


Dependency-based techniques can also be highly effective for ad hoc information retrieval (IR) (Park et al., 2011).

few path-based methods have been explored for ad hoc IR, largely because parsing large document collections is computationally prohibitive.


Syntactic language models for IR are a significant departure from this trend (Gao et al., 2004;Lee et al., 2006;Cai et al., 2007;Maisonnasse et al., 2007)   queries and documents to parent-child relations.

(Park et al., 2011) present a quasisynchronous translation model for IR that does not limit paths.


It also extends work for determining the variability of governor-dependent pairs (Song et al., 2008).

to this work, we apply linguistic features that are specific to catenae and dependency paths, and select among units containing more than two content-bearing words.


A dependency path is ordered and includes both word tokens and the relations between them.

a catena is a set of word types that may be ordered or partially ordered.


This enables us to explore semantic classification features and is highly accurate.

any dependency parser may be applied instead.


For example, if a noun is modified by two coordinated adjectives, there is a (symmetric) coordination relation between the two conjuncts and two (asymmetric) dependency relations between the conjuncts and the noun.

as there is no obvious linguistic intuition telling us which tree-shaped CS encoding is better and since the degree of freedom has several dimensions, one can find a number of distinct conventions introduced in particular dependency treebanks.


13 Most state-of-the-art dependency parsers can produce labeled edges.

the parsers produce only one label per edge.


To address the low-recall issue, recurring cue terms occurring within dictionary and encyclopedic resources can be automatically extracted and incorporated into lexical patterns (Saggion, 2004).

this approach is term-specific and does not scale to arbitrary terminologies and domains.


The results shows high precision in both cases.

these approaches to glossary learning extract unrestricted textual definitions from open text.


Similarly to our approach, they drop the requirement of a domain corpus and start from a small number of (term, hypernym) seeds.

while Doubly-Anchored Patterns have proven useful in the induction of domain taxonomies (Kozareva and Hovy, 2010a), they cannot be applied to the glossary learning task, because the extracted sentences are not formal definitions.


Plain annotation is the most common form of annotation and it is the one we shall focus on in this paper.

other, more complex, forms of annotation are also possible and of interest.


In other words, we face the following dilemma: • On the one hand, we should choose a small set ANN (i.e., select few annotators to base our collective annotation on), as that will allow us to increase the (average) reliability of the annotators taken into account.

• we should choose a large set ANN (i.e., select many annotators to base our collective annotation on), as that will increase the amount of information exploited.


12 All these efforts face the problem of how to aggregate the information provided by a group of volunteers into a collective annotation.

by and large, the emphasis so far has been on issues such as experiment design, data quality, and costs, with little attention being paid to the aggregation methods used, which are typically limited to some form of majority vote (or taking averages if the categories are numeric).


The English not in (6) functions as an adverbial adjunct that modifies the main verb (see top part of Figure 6) and information would be lost if this were not represented at f-structure.

the same cannot be said of the negative affix in Turkish -the morphological affix is not an adverbial adjunct.


The copula takes a non-finite complement whose subject is raised to the matrix clause as a non-thematic subject of the copula.

in Urdu (Figure 8), the Figure 6: Different f-structural analyses for negation (English vs. Turkish) copula is a two-place predicate, assigning SUBJ and PREDLINK functions.


Distributional thesauri are now widely used in a large number of Natural Language Processing tasks.

they are far from containing only interesting semantic relations.


The only exceptions are the P@1 values for M and WM as reference.

it should be noted that values for both MAP and R-precision, which are more reliable measures than P@1, are identical for the two thesauri and the same references.


(2008): they use a manually-constructed lexicon for Hebrew in order to learn an HMM tagger.

this lexicon was constructed by trained lexicographers over a long period of time and achieves very high coverage of the language with very good quality, much better than could be achieved by our non-expert linguistics graduate student annotators in just a few hours.


Comparing the tag dictionary entries versus the test data, precision starts in the high 80%s and falls to to the mid-70%s in all cases.

the differences in recall, shown in Table 2, are more interesting.


In their experiments, they-like usfound that type information is more valuable than token information.

they were able to see gains through the complementary effects of mixing type and token annotations.


Finally, additional raw text does improve performance.

using substantial amounts of raw text is unlikely to produce gains larger than only a few hours spent annotating types.


( 3) In all four examples, the verb and the participating noun phrases Mitarbeiter (employee), Kollege (colleague) and Bericht (report) are identical, and the noun phrases are assigned the same case.

given that the stemmed output of the translation does not tell us anything about case features, in order to predict the appropriate cases of the three noun phrases, we either rely on ordering heuristics (such that the nominative NP is more likely to be in the beginning of the sentence (the German Vorfeld) than the accusative or dative NP, even though all three of these would be grammatical), or we need fine-grained subcategorization information beyond pure syntax.


On the one hand, this has led to a range of manually or semi-automatically developed lexical resources focusing on verb information, such as the Levin classes (Levin, 1993), VerbNet (Kipper Schuler, 2006), FrameNet 4 (Fillmore et al., 2003), and PropBank (Palmer et al., 2005).

we find automatic approaches to the induction of verb subcategorization information at the syntax-semantics interface for a large number of languages, e.g.


The probabilities are discretized into 5 buckets (B p=0 , B 0<p≤0.25 , B 0.25<p≤0.5 , B 0.5<p≤0.75 , B 0.75<p≤1 ).

noun modification in noun-noun Gen construction is represented by cooccurrence frequencies.


For example, incorrect translations of "the" and "Bush" will receive the same penalty.

for crosslingual information processing applications, we should acknowledge that certain informationally critical words are more important than other common words.


(2012) proposed a semantic role driven MT metric.

none of these work declaratively exploited results from information extraction for MT.


(2004) that assigns higher likelihood to objects with larger similarity to share the same label.

q b (F ) corresponds to their fitting constraint, which requires the final alignment to maintain the maximum consistency with the initial alignment.


It is founded on the observation that true monotonic alignment paths usually lie close to the diagonal of a relation matrix.

it is not applicable to our task due to the nonmonotonicity involved.


T HB achieves a better MRR than T P H+P due to the semantic translation of organization names.

despite the increased recall of T HB over that of T Dict , the precision of T HB is unsatisfactory because T HB maps abbreviated names such as 'WTO' with other NEs.


However, despite the increased recall of T HB over that of T Dict , the precision of T HB is unsatisfactory because T HB maps abbreviated names such as 'WTO' with other NEs.

our method achieves the highest MRR and precision in both the person and organization categories.


Wikipedia infoboxes are a valuable source of structured knowledge for global knowledge sharing.

infobox information is very incomplete and imbalanced among the Wikipedias in different languages.


Hence, the challenge remains: how could we supplement the missing infoboxes for the rest 79.57% articles?

the numbers of existing infobox attributes in different languages are highly imbalanced.


Using variants of Wu and Palmer (1994) on the 374 SAT analogies of Turney (2005), Veale (2004) reports a success rate of 38-44% using only WordNet-based similarity.

turney (2005) reports up to 55% success on the same analogies, partly because his approach aims to match implicit relations rather than explicit concepts, and in part because it uses a divergent process to gather from the web as rich a perspective as it can on these latent relationships.


This poses a problem for the generation of sensible comparisons.

rex's lookup table captures the implicit pragmatics of comparability, making rex usable in generative tasks where a metric must both suggest and evaluate comparisons.


Yet other approaches extend topic models to produce author specific topics (Rosen-Zvi et al., 2004), author persona (Mimno and McCallum, 2007), social roles (McCallum et al., 2007), etc.

these models do not model debates and hence are unable to discover AD-expressions and interaction natures of author pairs.


Burfoot et al., (2011) builds on the work of (Thomas et al., 2006) and proposes collective classification using speaker contextual features (e.g., speaker intentions based on vote labels).

above works do not discover pair interactions (arguing nature) in debate authors.


For quantitative evaluation, topic models are often compared using perplexity.

perplexity does not reflect our purpose since we are not trying to evaluate how well the ADexpressions in an unseen discussion data fit our learned models.


There has been a substantial body of work on metaphor identification and interpretation (Wilks, 2007;.

in this paper we focus on an equally interesting, challenging and important problem, which concerns the automatic identification of affect carried by metaphors.


The previously proposed methods still suffer from the same issue of data sparseness when applied to MSD tagging.

in our approach, we overcome the problem through a different encoding of the input data (see section 2.1).


Probably the most reliable way to obtain such data would be to let annotators manually encode a training corpus with such transformations.

the task would be extremely tedious and the annotators would probably have to undergo special training (to be able to think in terms of transformations).


Since our approach requires manual involvement in the filtering of the attribute list, one might argue that one should simply manually enumerate the most relevant attributes directly.

the manual generation of conceptual features by a single researcher results in substantial variability both across and within participants (McRae et al., 2005).


With the increasing amount of user generated reference texts in the web, automatic quality assessment has become a key challenge.

only a small amount of annotated data is available for training quality assessment systems.


Automatic quality assessment has therefore become a key application in today's information society.

there is a lack of training data annotated with fine-grained quality information.


The average performance based on NOWIKI is slightly lower while using ALL features results in slightly higher average F 1 -scores.

the differences are not statistically significant and thus omitted.


If " •" ("don't") and " †" (past tense marker) are correctly recognized as two words, we may predict the previously unseen characters "g " ("tell spoilers") as an informal word, based on the learned Chinese language patterns.

state-of-the-art Chinese segmenters 1 incorrectly yield " • g †", preferring to chunk " †" ("thoroughly") as a word, as they do not consider the possibility that "g " ("spoiler") could be an informal word.


Other alternative frameworks such as Markov Logic Networks (MLNs) and Integer Linear Programming (ILP) could also be considered.

we feel that for this task, formulating efficient global formulas (constraints) for MLN (ILP) is comparatively less straightforward than in other tasks (e.g, compared to Semantic Role Labeling, where the rules may come directly from grammatical constraints).


This makes it a useful signal for IWR, as it is sensitive to informal words which often have low frequency.

the word frequency alone is not reliable enough to distinguish informal words from uncommon but formal words.


For RQ3, interestingly, the difference in performance between the LCRF and FCRF upper-bound systems is not significant.

these are upper bounds, and we expect on real-world data that CWS performance will not be perfect.


Answers and Baidu Zhidao and sites that are specialized in polls, such as Toluna.

it is highly unlikely that such sources will contain enough relevant questions for any news article due to typical sparseness issues as well as differences in interests between askers in CQA sites and news reporters.


Further delving into question correctness, the above example shows the need to assess each entity by itself.

even if both entities are independently valid for the template, their comparison may not make sense.


Finally, several works present unsupervised methods for ranking proper template instantia-tions, mainly as selectional preferences (Light and Greiff, 2002;Erk, 2007;Ritter et al., 2010).

we eventually choose instantiation candidates, and thus preferred supervised methods that enable filtering and not just ranking.


The baseline gets a little higher recall than our method, which shows the baseline method tends to make aggressive segmentation decisions.

both precision and F1 score of our method are much higher than the baseline.


The neural network model is a reasonable method to overcome these pitfalls.

the neural network based machine translation is far from easy.


Those systems use synchronous context-free grammars (Chiang, 2007), synchronous tree substitution grammars (Eisner, 2003) or even more powerful formalisms like synchronous tree-sequence substitution grammars (Sun et al., 2009).

those systems use linguistic syntactic annotation at different levels.


If we meet a lexical element (terminal), then we add it to the end of w i .

if we meet a nonterminal, then we have to consult the best pre-translation τ = t , (u 1 , .


So far, the LM scorer could only score their associated unigrams.

we also have their associated strings w 1 and w 2 , which can now be used.